• Laser & Optoelectronics Progress
  • Vol. 58, Issue 20, 2015003 (2021)
Chaodong Dai1、2, Guoliang Xu2、*, Jiao Mao1、2, Tong Gu1、2, and Jiangtao Luo2
Author Affiliations
  • 1College of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
  • 2Institute of Electronic Information and Network Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
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    DOI: 10.3788/LOP202158.2015003 Cite this Article Set citation alerts
    Chaodong Dai, Guoliang Xu, Jiao Mao, Tong Gu, Jiangtao Luo. Cell Phone Screen Defect Segmentation Based on Unsupervised Network[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2015003 Copy Citation Text show less

    Abstract

    Based on an unsupervised network, a method for cell phone screen defect segmentation is proposed to solve the problem of low accuracy in cell phone screen defect detection. First, an image reconstruction network with multiscale features is constructed through an unsupervised convolutional denoising autoencoder, which reconstructs the multilayer background texture image from the defect image. Then, the defect and multilayer-reconstructed images are subtracted separately to eliminate the influence of the background texture. Finally, adaptive threshold strategy is used for segmentation and the segmentation results are fused to improve the accuracy of defect segmentation. To improve the reconstruction performance, an improved loss function is proposed to train the reconstruction network. Based on an image pixel histogram, the triangle method is used for global adaptive threshold segmentation to improve the segmentation accuracy. The experimental result shows that the proposed method can predict the cell phone screen defect area, reaching 90.30% accuracy. The accuracy and real time of the proposed method meet industrial requirements and it is practical.
    Chaodong Dai, Guoliang Xu, Jiao Mao, Tong Gu, Jiangtao Luo. Cell Phone Screen Defect Segmentation Based on Unsupervised Network[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2015003
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